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(8/16/2016): Two additional sets of NLSY97 variables are being made available for download. These variables will be incorporated into NLS Investigator in the future. Downloaded files can be merged with other NLSY97 data using the PUBID (R0000100).

Dialect Density Measures (SPCH Variables)

Four additional variables are being made available in the Round 15 SPCH category. (See Speech Data in the NLSY97 for more information about this category). The additional variables are dialect density measures (DDMs) generated from R15 audio recordings.

- JS_words DDM: Count of words in Job Search response

- JS_tokens DDM: Count of AAVE tokens in Job Search response

- HM_words DDM: Count of words in Happiest Moment responses

- HM_tokens DDM: Count of AAVE tokens in Happiest Moment response

The DDM is the ratio of the number of African American Vernacular English (AAVE) tokens in the audio file to the number of words in the audio file; tokens and words are provided for both the Happiest Moment (HM) and Job Search (JS) prompts. Many Rs with valid audio recordings, mostly non-Southern whites and others, lack DDMs due to budget limitations.

Several new variables have been constructed from the NLSY97 post-secondary transcripts. These variables extract information from course title and number information to assist researchers in understanding course-taking behavior without jeopardizing respondent and institution confidentiality. See Appendix 12: Post- Secondary Transcript Study for more information about this category.

A new data file contains the following variables (# indexes the course number, which ranges from 1 to 198):

Four flags provide information about the verbatim course title appearing in the transcript. These flags simply indicate presence of specific character strings in the course title field on the transcript. In conjunction with other variables, these flags may assist in identifying certain types of courses, although the flags on their own are likely to identify course types with error.

The course title flags and an additional flag have values:

1 Course flagged

0 Course not flagged.

Reserved codes (-1 through -5) are defined consistent with other PSTRAN course-level variables.

PSTRAN_CRS_TITLE_DEV_

Course title flag developmental – course had one or more of the following terms in its course title (* indicates that misspellings and variants of the term were also accepted):

REMEDIAL*, REMED

BASIC with COL* or SKILL* or ALGEBRA, WRITING, MATH*

DEVELOPMENTAL or DEVELOP, except when the course title also includes BIO*, PSY*, or ECON*

The CCM6 and CCM2 variable sets replace existing variables of the same names. Code values can be found here: http://nces.ed.gov/pubs2012/2012162rev.pdf. The replacement variables differ from 32.0108 ‘Developmental/Remedial English’ to 23.1301 ‘Writing, General.’ Recoded courses can be identified using the variable set CCM6_R_Flag.

The availability of the course title flags allows researchers to identify family 23 courses that may be remedial. These courses were originally assigned to the remedial CCM category 32 because they could not otherwise have been identified as potentially remedial. This edit affects 3,500 of the 129,000 courses in the file.

Although no recoding has been done for mathematics courses, we add the following notes regarding CCM codes for remedial math:

The classification of a course as remedial can be inconsistent across institutions, across units within an institution, and even for a given student at an institution. For example, the same course title may be designated as remedial at one institution and not at another. Within institutions, an algebra course may be considered remedial for an engineering student but not for a marketing student. Even for a given individual, changing majors from engineering to marketing can change whether or not a course earns credit towards a degree. In other cases, a passing grade in a course may be required to be accompanied by a certain score on a placement test in order for a course to be designated as non-remedial. These issues are most germane to English and Mathematics courses. To provide the maximum flexibility to researchers, we have taken the following steps to classifying courses and offering researchers the potential to designate courses as remedial according to different research specifications:

1) Courses that could potentially be remedial math courses based on their course titles are assigned to category 32.0104.

2) Courses that were specifically designated as remedial on the transcripts through course titles, grade annotations, or other notes, are indicated as such in the variable series REM_ANN.

3) Courses with a leading zero in the course number (for example, English 099) are indicated as such in the variable series NUMLEAD0.

4) Courses with specific key words in course titles often associated with remedial courses are indicated as such in the variable series TITLE_DEV

In addition to these, researchers may want to consider such criteria as:

1) term number in which the course was taken (remedial courses are often taken earlier in enrollment at an institution), and

2) whether or not a passing grade was associated with positive credits earned (remedial courses may not always earn degree credit depending on the rules of an institution).

Depending on the research question of interest, users may choose to use some combination of these criteria to differentiate remedial courses from non-remedial courses. Researchers may want to re-code to ‘27.0101 General Mathematics’ the CCM_6 value of any course that is coded as ‘32.0104 Developmental/Remedial Mathematics’ but not considered as remedial in a given analysis.